FDA Radiology AI Devices

Stone Clear is a medical device that uses ultrasound imaging to locate urinary stones in the upper urinary tract and applies focused ultrasound pulses to reposition these stones, helping patients pass them more easily after lithotripsy treatment. It provides real-time image guidance and therapy, improving stone management and patient outcomes.

The ShortCut device by Pi-Cardia Ltd. is a single-use catheter designed to safely split bioprosthetic aortic valve leaflets to facilitate valve-in-valve procedures in patients at risk of coronary obstruction. This catheter-based tool is inserted via the femoral artery and positions its distal cutting element on valve leaflets to perform precise cuts, helping improve blood flow and reduce complications during transcatheter valve replacement procedures. It has been demonstrated in clinical studies to be effective and safe, providing a new treatment option for high-risk patients undergoing valve-in-valve procedures.

DentalMonitoring is a medical software device that uses AI to analyze pictures of a patient's mouth taken via smartphone and proprietary hardware. It helps dental professionals remotely monitor orthodontic treatments by assessing oral hygiene, soft tissue, dental conditions, alignment, and occlusion. It provides 2D and 3D monitoring capabilities to track treatment progress safely and non-invasively.

The Notal Vision Home OCT System is an AI-based home use device that enables patients with neovascular age-related macular degeneration (NV-AMD) to self-image their eyes at home using Optical Coherence Tomography (OCT). It captures detailed images of the retina, segments and quantifies hypo-reflective spaces associated with fluid build-up, and securely transmits data for physician review between scheduled clinic visits. This allows more frequent monitoring of eye disease progression without replacing standard clinical examinations.

Rho is a machine learning software that analyzes standard frontal x-rays of various body parts in patients aged 50 and older to identify possible low bone mineral density. It helps radiologists by generating reports suggesting patients who may benefit from further clinical bone health assessment, thereby assisting in early detection and management of bone health issues.

The Genius Digital Diagnostics System with the Genius Cervical AI algorithm is an AI-powered digital imaging system designed to scan, analyze, and help interpret ThinPrep Pap test slides for cervical cancer screening. It identifies clinically relevant cellular objects and presents them in an easy-to-review format for cytologists and pathologists, improving diagnostic sensitivity while assisting in patient management.

DermaSensor is a handheld device combined with a base unit that uses optical spectroscopy and an AI/machine learning algorithm to analyze suspicious skin lesions and assist physicians, especially those not trained as dermatologists, in deciding whether a patient should be referred to a dermatologist. It provides a classification indicating whether to monitor the lesion or investigate further, helping clinicians detect common skin cancers earlier and more accurately.

Fibresolve is a software device that analyzes lung CT images using deep learning to identify patterns suggestive of idiopathic pulmonary fibrosis. It provides a binary output to assist clinicians in diagnosing fibrotic lung disease, serving as an adjunct tool in the diagnostic workflow especially before invasive testing, helping to improve early detection and referral for appropriate clinical discussion.

BrainSee is software that helps clinicians predict whether patients aged 55 to 95 with amnestic mild cognitive impairment (aMCI) will progress to clinical Alzheimer's disease dementia within 5 years. It uses standard brain MRI scans along with patient demographics and cognitive test scores to generate a similarity score indicating the likelihood of progression. This tool provides supplemental prognostic information to aid clinical decision-making but is not a standalone diagnostic device.

Viz HCM is a machine learning-based software designed to analyze 12-lead ECG recordings to detect signs of hypertrophic cardiomyopathy (HCM). It helps clinicians identify patients who may have HCM and need further clinical follow-up, supporting early detection though not replacing full diagnostic evaluation. The system includes an ECG analysis algorithm and a mobile app to present results, improving screening in routine cardiology care.